Cost-Effectiveness Acceptability Frontier (CEAF) plot

Produces a plot the Cost-Effectiveness Acceptability Frontier (CEAF) against the willingness to pay threshold.

Usage

# S3 method for pairwise
ceaf.plot(mce, graph = c("base", "ggplot2"), ...)

ceaf.plot(mce, ...)

Arguments

mce: The output of the call to the function multi.ce()
graph: A string used to select the graphical engine to use for plotting. Should (partial-) match the two options "base" or "ggplot2". Default value is "base".
...: Additional arguments

Value

ceaf: A ggplot object containing the plot. Returned only if graph="ggplot2".

References

Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/.

Baio G (2013). Bayesian Methods in Health Economics. CRC.

Author

Gianluca Baio, Andrea Berardi

Examples


# See Baio G., Dawid A.P. (2011) for a detailed description of the 
# Bayesian model and economic problem

# Load the processed results of the MCMC simulation model
data(Vaccine)

# Runs the health economic evaluation using BCEA
m <- bcea(
      e=eff,
      c=cost,               # defines the variables of 
                            #  effectiveness and cost
      ref=2,                # selects the 2nd row of (e, c) 
                            #  as containing the reference intervention
      interventions=treats, # defines the labels to be associated 
                            #  with each intervention
      Kmax=50000,           # maximum value possible for the willingness 
                            #  to pay threshold; implies that k is chosen 
                            #  in a grid from the interval (0, Kmax)
      plot=FALSE            # inhibits graphical output
)

# \donttest{
mce <- multi.ce(m)          # uses the results of the economic analysis 
# }

# \donttest{
ceaf.plot(mce)              # plots the CEAF 

# }

# \donttest{
ceaf.plot(mce, graph = "g") # uses ggplot2 

# }

# \donttest{
# Use the smoking cessation dataset
data(Smoking)
m <- bcea(eff, cost, ref = 4, intervention = treats, Kmax = 500, plot = FALSE)
mce <- multi.ce(m)
ceaf.plot(mce)

# }